RA-ACS_net Network: A Quantum Optical Reconstruction Method for Ultra-high Resolution Bioimaging

Ultra-high resolution bioimaging based on quantum optics offers high sensitivity at relatively low cost, yet conventional reconstruction algorithms face challenges of excessive sampling time, long computation, and artifacts that limit imaging quality. To overcome these issues, this study proposes a novel quantum optical bioimaging reconstruction method termed RA-ACS_net, which integrates a ripple algorithm with a hybrid attention mechanism network. The ripple algorithm provides global optimization for network parameter adjustment, while the attention mechanism enhances feature extraction and information fusion. Furthermore, a differentiated loss function (ALoss) is designed to preserve fine structural details and improve visual fidelity compared with conventional MSE loss. A large-scale dataset of quantum optics-based bioimages is employed for training and validation. Experimental results demonstrate that RA-ACS_net achieves superior reconstruction performance, with significantly higher PSNR and SSIM across both low and high sampling ratios, when compared to iterative algorithms (TVAL3) and existing deep learning models (DR2-Net, DPA-Net). The proposed approach exhibits robustness under sparse data conditions, reduces blocking artifacts, and accelerates convergence, thereby addressing critical limitations of current methods. This study highlights the potential of combining quantum optics with advanced deep learning optimization strategies to establish a practical and efficient framework for ultra-high resolution bioimaging.

@article{SHANG2025,
  title     = {RA-ACS_net Network: A Quantum Optical Reconstruction Method for Ultra-high Resolution Bioimaging},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Lin SHANG},
  doi       = {10.14569/IJACSA.2025.0160847},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160847}
}